Papers citing ALKEMIE: An intelligent computational platform for accelerating materials discovery and design
Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design
Molecules · 2026
Tongbaihui Qi, Jie Zhou
Read original paper ↗ ·Enhancing high-throughput battery materials discovery and design with artificial intelligence
eScience · 2026
Qian Zhao, Bing He, Yurong Ren, Maxim Avdeev, Yuxiao Lin, Siqi Shi
Read original paper ↗ ·Zentropy Theory in Materials Science: Challenges and Opportunities
AI Materials · 2026
Shucheng Xing, Jian Xin Zhou, Zhimei Sun
Read original paper ↗ ·A Novel Method for Predicting Material Properties and Casting Defects of Ferritic Nodular Cast Iron Based on the Machine Learning and Integrated Computing
Metallurgical and Materials Transactions A · 2026
Pan Zhang, Donghong Wang, Xiaoming Deng, Xiaojiang Xia, Baode Sun
Read original paper ↗ ·Machine learning interatomic potentials for high-entropy alloys: from atomic mechanisms to macroscopic design
Computational Materials Science · 2026
Abdul Bari, Jiankang Huang, Muhammad Ishfaq, Chen Dong, Shichen Yan, Ahtisham Karim
Read original paper ↗ ·